Data Scientist - Machine Learning, Search & Personalization

This Turn2 client is a rapidly scaling technology company that is seeking a Data Scientist to design and deploy intelligent systems that shape how users search, discover, and interact with digital products. This role sits at the intersection of research and product, ideal for someone eager to solve hard problems at scale using applied ML.

You'll lead the development of real-time ranking, recommendation, and personalization models, working closely with engineering and product stakeholders to drive measurable user impact.

Why This Role Stands Out:

• Core impact: Shape how users experience discovery and decision-making across the platform.
• End-to-end ownership: From model design to production deployment and performance optimization.
• Innovation-driven: Work with modern ML approaches-embeddings, ranking systems, and real-time inference.

What You'll Do:

• Build machine learning models for search relevance, ranking, and personalized recommendations.
• Develop and scale retrieval systems to support fast, relevant product discovery.
• Design full ML pipelines from data ingestion and feature engineering to model training and deployment.
• Experiment with embedding techniques, ranking algorithms, and personalization methods.
• Collaborate with engineering and product teams to embed models into core user-facing experiences.

What You Bring:

• 5+ years of hands-on experience in applied machine learning.
• Deep knowledge of recommendation systems, search, personalization, or ranking models.
• Strong Python skills and familiarity with ML libraries (e.g., PyTorch, TensorFlow).
• Experience with distributed data processing (e.g., PySpark, ETL pipelines).
• Track record of deploying machine learning models into production systems.

Bonus Points For:

• Advanced degree in Computer Science, Data Science, or a related technical field.
• Experience with cloud platforms like AWS, Google Cloud Platform, or Azure.
• Familiarity with real-time inference systems or large-scale retrieval architectures.
• Ability to bridge ML research and product needs-delivering practical solutions with business impact.